Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social network. This approach is mainly grounded upon the correct usage of three basic graph-theoretic measures: Degree centrality, Closeness centrality and Betweenness centrality. We show that, in general, those indices are not adapt to foresee the flow of a given message, which depends upon indices based on the sharing of interests and the trust about depth in knowledge of a topic.We provide an extended model, that is a simplified version of a more general model already documented in the literature, the Semantic Social Network Analysis, and we show that by means of this model it is possible to exceed the drawbacks of general indices discussed above.
Semantic social network analysis foresees message flows
CRISTANI, Matteo;TOMAZZOLI, Claudio;OLIVIERI, FRANCESCO
2016-01-01
Abstract
Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social network. This approach is mainly grounded upon the correct usage of three basic graph-theoretic measures: Degree centrality, Closeness centrality and Betweenness centrality. We show that, in general, those indices are not adapt to foresee the flow of a given message, which depends upon indices based on the sharing of interests and the trust about depth in knowledge of a topic.We provide an extended model, that is a simplified version of a more general model already documented in the literature, the Semantic Social Network Analysis, and we show that by means of this model it is possible to exceed the drawbacks of general indices discussed above.
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